def exercise(file_name=None, pdb_file_name = None, map_file_name = None ,
split_pdb_file_name = None,
out = sys.stdout):
# Set up source data
if not os.path.isfile(file_name):
raise Sorry("Missing the file: %s" %(file_name)+"\n")
print ("Reading from %s" %(file_name))
from iotbx.map_manager import map_manager
m = map_manager(file_name)
print ("Header information from %s:" %(file_name))
m.show_summary(out = out)
map_data = m.map_data().deep_copy()
crystal_symmetry = m.crystal_symmetry()
unit_cell_parameters = m.crystal_symmetry().unit_cell().parameters()
print ("\nMap origin: %s Extent %s" %( map_data.origin(), map_data.all()))
print ("Original unit cell, not just unit cell of part in this file): %s" %(
str(unit_cell_parameters)))
grid_point = (1, 2, 3)
if map_data.origin() != (0, 0, 0): # make sure it is inside
from scitbx.matrix import col
grid_point = tuple (col(grid_point)+col(map_data.origin()))
print ("\nValue of map_data at grid point %s: %.3f" %(str(grid_point),
map_data[grid_point]))
print ("Map data is %s" %(type(map_data)))
random_position = (10, 5, 7.9)
point_frac = crystal_symmetry.unit_cell().fractionalize(random_position)
value_at_point_frac = map_data.eight_point_interpolation(point_frac)
print ("Value of map_data at coordinates %s: %.3f" %(
str(random_position), value_at_point_frac))
map_data_as_float = map_data.as_float()
print ("Map data as float is %s" %(type(map_data_as_float)))
# make a little model
sites_cart = flex.vec3_double( ((8, 10, 12), (14, 15, 16)))
model = model_manager.from_sites_cart(
atom_name = ' CA ',
resname = 'ALA',
chain_id = 'A',
b_iso = 30.,
occ = 1.,
scatterer = 'C',
sites_cart = sites_cart,
crystal_symmetry = crystal_symmetry)
# Move map and a model to place origin at (0, 0, 0)
# map data is new copy but model is shifted in place.
from iotbx.map_model_manager import map_model_manager
mam = map_model_manager(
map_manager = m,
model = model.deep_copy(),
)
# Read in map and model and split up
dm = DataManager()
aa = dm.get_map_model_manager(model_file=pdb_file_name,
map_files=map_file_name)
bb = dm.get_map_model_manager(model_file=split_pdb_file_name,
map_files=map_file_name)
for selection_method in ['by_chain', 'by_segment','supplied_selections',
'boxes']:
if selection_method == 'boxes':
choices = [True, False]
else:
choices = [True]
if selection_method == 'by_chain':
mask_choices = [True,False]
else:
mask_choices = [False]
for select_final_boxes_based_on_model in choices:
for skip_empty_boxes in choices:
for mask_choice in mask_choices:
if mask_choice: # use split model
a=bb.deep_copy()
else: # usual
a=aa.deep_copy()
print ("\nRunning split_up_map_and_model with \n"+
"select_final_boxes_based_on_model="+
"%s skip_empty_boxes=%s selection_method=%s" %(
select_final_boxes_based_on_model,skip_empty_boxes,selection_method))
if selection_method == 'by_chain':
print ("Mask around unused atoms: %s" %(mask_choice))
box_info = a.split_up_map_and_model_by_chain(
mask_around_unselected_atoms=mask_choice)
elif selection_method == 'by_segment':
box_info = a.split_up_map_and_model_by_segment()
elif selection_method == 'supplied_selections':
selection = a.model().selection('all')
box_info = a.split_up_map_and_model_by_supplied_selections(
selection_list = [selection])
elif selection_method == 'boxes':
box_info = a.split_up_map_and_model_by_boxes(
skip_empty_boxes = skip_empty_boxes,
select_final_boxes_based_on_model =
select_final_boxes_based_on_model)
print (selection_method,skip_empty_boxes,
len(box_info.selection_list),
box_info.selection_list[0].count(True))
assert (selection_method,skip_empty_boxes,
len(box_info.selection_list),
box_info.selection_list[0].count(True)) in [
('by_chain',True,3,19),
("by_chain",True,1,86,),
("by_segment",True,1,86,),
("supplied_selections",True,1,86,),
("boxes",True,13,1,),
("boxes",False,36,0,),
("boxes",True,13,1,),
("boxes",False,36,0,),
]
# Change the coordinates in one box
small_model = box_info.mmm_list[0].model()
small_sites_cart = small_model.get_sites_cart()
from scitbx.matrix import col
small_sites_cart += col((1,0,0))
small_model.set_crystal_symmetry_and_sites_cart(
sites_cart = small_sites_cart,
crystal_symmetry = small_model.crystal_symmetry())
# Put everything back together
a.merge_split_maps_and_models(box_info = box_info)
mam.box_all_maps_around_model_and_shift_origin()
shifted_crystal_symmetry = mam.model().crystal_symmetry()
shifted_model = mam.model()
shifted_map_data = mam.map_data()
print ("\nOriginal map origin (grid units):", map_data.origin())
print ("Original model:\n", model.model_as_pdb())
print ("Shifted map origin:", shifted_map_data.origin())
print ("Shifted model:\n", shifted_model.model_as_pdb())
# Save the map_model manager
mam_dc=mam.deep_copy()
print ("dc",mam)
print ("dc mam_dc",mam_dc)
# Mask map around atoms
mam=mam_dc.deep_copy()
print ("dc mam_dc dc",mam_dc)
print (mam)
mam.mask_all_maps_around_atoms(mask_atoms_atom_radius = 3,
set_outside_to_mean_inside=True, soft_mask=False)
print ("Mean before masking", mam.map_data().as_1d().min_max_mean().mean)
assert approx_equal(mam.map_data().as_1d().min_max_mean().mean,
-0.0585683621466)
print ("Max before masking", mam.map_data().as_1d().min_max_mean().max)
assert approx_equal(mam.map_data().as_1d().min_max_mean().max,
-0.0585683621466)
# Mask map around atoms, with soft mask
mam=mam_dc.deep_copy()
mam.mask_all_maps_around_atoms(mask_atoms_atom_radius = 3, soft_mask = True,
soft_mask_radius = 5, set_outside_to_mean_inside=True)
print ("Mean after first masking", mam.map_data().as_1d().min_max_mean().mean)
assert approx_equal(mam.map_data().as_1d().min_max_mean().mean,
-0.00177661714805)
print ("Max after first masking", mam.map_data().as_1d().min_max_mean().max)
assert approx_equal(mam.map_data().as_1d().min_max_mean().max,
0.236853733659)
# Mask map around atoms again
mam.mask_all_maps_around_atoms(mask_atoms_atom_radius = 3,
set_outside_to_mean_inside = True, soft_mask=False)
print ("Mean after second masking", mam.map_data().as_1d().min_max_mean().mean)
assert approx_equal(mam.map_data().as_1d().min_max_mean().mean,
-0.0585683621466)
print ("Max after second masking", mam.map_data().as_1d().min_max_mean().max)
assert approx_equal(mam.map_data().as_1d().min_max_mean().max,
-0.0585683621466)
# Mask around edges
mam=mam_dc.deep_copy()
mam.mask_all_maps_around_edges( soft_mask_radius = 3)
print ("Mean after masking edges", mam.map_data().as_1d().min_max_mean().mean)
assert approx_equal(mam.map_data().as_1d().min_max_mean().mean,
0.0155055604192)
print ("Max after masking edges", mam.map_data().as_1d().min_max_mean().max)
assert approx_equal(mam.map_data().as_1d().min_max_mean().max,
0.249827131629)
print ("\nWriting map_data and model in shifted position (origin at 0, 0, 0)")
output_file_name = 'shifted_map.ccp4'
print ("Writing to %s" %(output_file_name))
mrcfile.write_ccp4_map(
file_name = output_file_name,
crystal_symmetry = shifted_crystal_symmetry,
map_data = shifted_map_data, )
output_file_name = 'shifted_model.pdb'
f = open(output_file_name, 'w')
print (shifted_model.model_as_pdb(), file=f)
f.close()
print ("\nWriting map_data and model in original position (origin at %s)" %(
str(mam.map_manager().origin_shift_grid_units)))
output_file_name = 'new_map_original_position.ccp4'
print ("Writing to %s" %(output_file_name))
mrcfile.write_ccp4_map(
file_name = output_file_name,
crystal_symmetry = shifted_crystal_symmetry,
map_data = shifted_map_data,
origin_shift_grid_units = mam.map_manager().origin_shift_grid_units)
print (shifted_model.model_as_pdb())
output_pdb_file_name = 'new_model_original_position.pdb'
f = open(output_pdb_file_name, 'w')
print (shifted_model.model_as_pdb(), file=f)
f.close()
# Write as mmcif
output_cif_file_name = 'new_model_original_position.cif'
f = open(output_cif_file_name, 'w')
print (shifted_model.model_as_mmcif(),file = f)
f.close()
# Read the new map and model
import iotbx.pdb
new_model = model_manager(
model_input = iotbx.pdb.input(
source_info = None,
lines = flex.split_lines(open(output_pdb_file_name).read())),
crystal_symmetry = crystal_symmetry)
assert new_model.model_as_pdb() == model.model_as_pdb()
new_model_from_cif = model_manager(
model_input = iotbx.pdb.input(
source_info = None,
lines = flex.split_lines(open(output_cif_file_name).read())),
crystal_symmetry = crystal_symmetry)
assert new_model_from_cif.model_as_pdb() == model.model_as_pdb()
# Read and box the original file again in case we modified m in any
# previous tests
m = map_manager(file_name)
mam=map_model_manager(model=model.deep_copy(),map_manager=m)
mam.box_all_maps_around_model_and_shift_origin()
file_name = output_file_name
print ("Reading from %s" %(file_name))
new_map = iotbx.mrcfile.map_reader(file_name = file_name, verbose = False)
new_map.data = new_map.data.shift_origin()
print ("Header information from %s:" %(file_name))
new_map.show_summary(out = out)
assert new_map.map_data().origin() == mam.map_manager().map_data().origin()
assert new_map.crystal_symmetry().is_similar_symmetry(mam.map_manager().crystal_symmetry())
# make a map_model_manager with lots of maps and model and ncs
from mmtbx.ncs.ncs import ncs
ncs_object=ncs()
ncs_object.set_unit_ncs()
mam = map_model_manager(
map_manager = m,
ncs_object = ncs_object,
map_manager_1 = m.deep_copy(),
map_manager_2 = m.deep_copy(),
extra_model_list = [model.deep_copy(),model.deep_copy()],
extra_model_id_list = ["model_1","model_2"],
extra_map_manager_list = [m.deep_copy(),m.deep_copy()],
extra_map_manager_id_list = ["extra_1","extra_2"],
model = model.deep_copy(),
)
# make a map_model_manager with lots of maps and model and ncs and run
# with wrapping and ignore_symmetry_conflicts on
from mmtbx.ncs.ncs import ncs
ncs_object=ncs()
ncs_object.set_unit_ncs()
m.set_ncs_object(ncs_object.deep_copy())
mam2 = map_model_manager(
map_manager = m.deep_copy(),
ncs_object = ncs_object.deep_copy(),
map_manager_1 = m.deep_copy(),
map_manager_2 = m.deep_copy(),
extra_model_list = [model.deep_copy(),model.deep_copy()],
extra_model_id_list = ["model_1","model_2"],
extra_map_manager_list = [m.deep_copy(),m.deep_copy()],
extra_map_manager_id_list = ["extra_1","extra_2"],
model = model.deep_copy(),
ignore_symmetry_conflicts = True,
wrapping = m.wrapping(),
)
assert mam.map_manager().is_similar(mam2.map_manager())
assert mam.map_manager().is_similar(mam2.map_manager_1())
for m in mam2.map_managers():
assert mam.map_manager().is_similar(m)
assert mam.model().shift_cart() == mam2.model().shift_cart()
assert mam.model().shift_cart() == mam2.get_model_by_id('model_2').shift_cart()
print ("OK")
def exercise(file_name, out=sys.stdout):
# Set up source data
if not os.path.isfile(file_name):
raise Sorry("Missing the file: %s" % (file_name) + "\n")
print("Reading from %s" % (file_name))
from iotbx.map_manager import map_manager
m = map_manager(file_name)
# make a little model
sites_cart = flex.vec3_double(((8, 10, 12), (14, 15, 16)))
model = model_manager.from_sites_cart(
atom_name=' CA ',
resname='ALA',
chain_id='A',
b_iso=30.,
occ=1.,
scatterer='C',
sites_cart=sites_cart,
crystal_symmetry=m.crystal_symmetry())
# make a map_model_manager with lots of maps and model and ncs
from iotbx.map_model_manager import map_model_manager
from mmtbx.ncs.ncs import ncs
ncs_object = ncs()
ncs_object.set_unit_ncs()
mask_mm = m.deep_copy()
mask_mm.set_is_mask(True)
mam = map_model_manager(
map_manager=m,
ncs_object=ncs_object,
map_manager_1=m.deep_copy(),
map_manager_2=m.deep_copy(),
extra_map_manager_list=[m.deep_copy(),
m.deep_copy(),
m.deep_copy()],
extra_map_manager_id_list=["extra_1", "extra_2", "map_manager_mask"],
model=model.deep_copy(),
)
print(mam.map_manager())
print(mam.model())
print(mam.map_manager_1())
print(mam.map_manager_2())
print(mam.map_manager_mask())
print(mam.map_manager().ncs_object())
all_map_names = mam.map_id_list()
for id in all_map_names:
print("Map_manager %s: %s " % (id, mam.get_map_manager_by_id(id)))
dm = DataManager(['model', 'miller_array', 'real_map', 'phil', 'ncs_spec'])
dm.set_overwrite(True)
# Create a model with ncs
from iotbx.regression.ncs.tst_ncs import pdb_str_5
file_name = 'tst_mam.pdb'
f = open(file_name, 'w')
print(pdb_str_5, file=f)
f.close()
# Generate map data from this model (it has ncs)
mmm = map_model_manager()
mmm.generate_map(box_cushion=0, file_name=file_name, n_residues=500)
ncs_mam = mmm.deep_copy()
ncs_mam_copy = mmm.deep_copy()
# Make sure this model has 126 sites (42 sites times 3-fold ncs)
assert ncs_mam.model().get_sites_cart().size() == 126
assert approx_equal(ncs_mam.model().get_sites_cart()[0],
(23.560999999999996, 8.159, 10.660000000000002))
# Get just unique part (42 sites)
unique_mam = ncs_mam.extract_all_maps_around_model(
select_unique_by_ncs=True)
assert unique_mam.model().get_sites_cart().size() == 42
assert approx_equal(unique_mam.model().get_sites_cart()[0],
(18.740916666666664, 13.1794, 16.10544))
# Make sure that the extraction did not change the original but does change
# the extracted part
assert (unique_mam.model().get_sites_cart()[0] !=
ncs_mam.model().get_sites_cart()[0]
) # it was a deep copy so original stays
# Shift back the extracted part and make sure it matches the original now
shifted_back_unique_model = mmm.get_model_from_other(
unique_mam.deep_copy())
assert approx_equal(shifted_back_unique_model.get_sites_cart()[0],
(23.560999999999996, 8.158999999999997, 10.66))
# Change the extracted model
sites_cart = unique_mam.model().get_sites_cart()
sites_cart[0] = (1, 1, 1)
unique_mam.model().get_hierarchy().atoms().set_xyz(sites_cart)
# Note; setting xyz in hierarchy does not set xrs by itself. do that now:
unique_mam.model().set_sites_cart_from_hierarchy(multiply_ncs=False)
# Make sure we really changed it
assert approx_equal(unique_mam.model().get_sites_cart()[0], (1, 1, 1))
# Now propagate all the changes in this unique part to entire original model
# using NCS
ncs_mam.propagate_model_from_other(other=unique_mam,
model_id='model',
other_model_id='model')
# ...and check that copy 1 and copy 2 both change
assert approx_equal(
ncs_mam.model().get_sites_cart()[0],
(5.820083333333333, -4.020400000000001, -4.445440000000001))
assert approx_equal(
ncs_mam.model().get_sites_cart()[42],
(38.41904613024224, 17.233251085893276, 2.5547442135142524))
# Find ncs from map or model
nn = ncs_mam_copy
nn.write_map('ncs.ccp4')
nn.write_model('ncs.pdb')
ncs_object = nn.get_ncs_from_model()
dm.write_ncs_spec_file(ncs_object, 'ncs.ncs_spec')
print("NCS from map", ncs_object)
nn.set_ncs_object(ncs_object)
print("NCS now: ", nn.ncs_object())
nn.get_ncs_from_map(ncs_object=ncs_object)
print("ncs cc:", nn.ncs_cc())
assert approx_equal(nn.ncs_cc(), 0.961915979834, eps=0.01)
# Make a deep_copy
dc = mam.deep_copy()
new_mam = mam.deep_copy()
assert mam.map_manager().map_data()[0] == new_mam.map_manager().map_data(
)[0]
# Make a customized_copy
new_mam = mam.customized_copy(model_dict={'model': mam.model()})
assert new_mam.model() is mam.model()
assert not new_mam.map_dict() is mam.map_dict()
new_mam = mam.customized_copy(model_dict={'model': mam.model()},
map_dict=mam.map_dict())
assert new_mam.model() is mam.model()
assert new_mam.map_dict() is mam.map_dict()
print(mam)
# Add a map
mam = dc.deep_copy()
print(mam.map_id_list())
assert len(mam.map_id_list()) == 6
mam.add_map_manager_by_id(mam.map_manager().deep_copy(), 'new_map_manager')
print(mam.map_id_list())
assert len(mam.map_id_list()) == 7
# duplicate a map
mam = dc.deep_copy()
print(mam.map_id_list())
assert len(mam.map_id_list()) == 6
mam.duplicate_map_manager('map_manager', 'new_map_manager')
print(mam.map_id_list())
assert len(mam.map_id_list()) == 7
# resolution_filter a map
mam = dc.deep_copy()
print(mam.map_id_list())
mam.duplicate_map_manager('map_manager', 'new_map_manager')
mam.resolution_filter(map_id='new_map_manager', d_min=3.5, d_max=6)
# Add a model
mam = dc.deep_copy()
print(mam.model_id_list())
assert len(mam.model_id_list()) == 1
mam.add_model_by_id(mam.model().deep_copy(), 'new_model')
print(mam.model_id_list())
assert len(mam.model_id_list()) == 2
# Initialize a map
mam1 = new_mam.deep_copy()
mam1.initialize_maps(map_value=6)
assert mam1.map_manager().map_data()[225] == 6
# Create mask around density and apply to all maps
mam1 = new_mam.deep_copy()
mam1.mask_all_maps_around_density(
solvent_content=0.5,
soft_mask=True,
)
s = (mam1.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (1024, 2048))
# Create mask around edges and apply to all maps
mam1 = new_mam.deep_copy()
mam1.mask_all_maps_around_edges()
s = (mam1.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (1176, 2048))
# Create a soft mask around model and apply to all maps
new_mam.mask_all_maps_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (1944, 2048))
# Create a soft mask around model and do not do anything with it
new_mam.create_mask_around_atoms(mask_atoms_atom_radius=8, soft_mask=True)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (1944, 2048))
# Create a soft mask around model and do not do anything with it, wrapping =true
dummy_mam = new_mam.deep_copy()
dummy_mam.map_manager().set_wrapping(True)
dummy_mam.create_mask_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (dummy_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (1944, 2048))
# Create a sharp mask around model and do not do anything with it
new_mam.create_mask_around_atoms(soft_mask=False, mask_atoms_atom_radius=8)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (138, 2048))
# Mask around edges and do not do anything with it
mam = dc.deep_copy()
mam.create_mask_around_edges()
s = (mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (1176, 2048))
# Mask around density and to not do anything with it
mam = dc.deep_copy()
mam.create_mask_around_density(soft_mask=False)
s = (mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal((s.count(True), s.size()), (1000, 2048))
# Apply the current mask to one map
mam.apply_mask_to_map('map_manager')
s = (mam.map_manager().map_data() > 0.)
assert approx_equal((s.count(True), s.size()), (640, 2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal((s.count(True), s.size()), (1000, 2048))
assert approx_equal((mam.map_manager().map_data()[225]), -0.0418027862906)
# Apply any mask to one map
mam.apply_mask_to_map('map_manager', mask_id='mask')
s = (mam.map_manager().map_data() > 0.)
assert approx_equal((s.count(True), s.size()), (640, 2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal((s.count(True), s.size()), (1000, 2048))
assert approx_equal((mam.map_manager().map_data()[225]), -0.0418027862906)
# Apply the mask to all maps
mam.apply_mask_to_maps()
s = (mam.map_manager().map_data() > 0.)
assert approx_equal((s.count(True), s.size()), (640, 2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal((s.count(True), s.size()), (1000, 2048))
assert approx_equal((mam.map_manager().map_data()[225]), -0.0418027862906)
# Apply the mask to all maps, setting outside value to mean inside
mam.apply_mask_to_maps(set_outside_to_mean_inside=True)
s = (mam.map_manager().map_data() > 0.)
assert approx_equal((s.count(True), s.size()), (1688, 2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal((s.count(True), s.size()), (2048, 2048))
assert approx_equal((mam.map_manager().map_data()[2047]), -0.0759598612785)
s = (mam.get_map_manager_by_id('mask').map_data() > 0).as_1d()
inside = mam.map_manager().map_data().as_1d().select(s)
outside = mam.map_manager().map_data().as_1d().select(~s)
assert approx_equal(
(inside.min_max_mean().max, outside.min_max_mean().max),
(0.335603952408, 0.0239064293122))
# Make a new map and model, get mam and box with selection
mmm = map_model_manager()
mmm.generate_map(box_cushion=0, wrapping=True)
mam = mmm
mam_dc = mam.deep_copy()
new_mm_1 = mam.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((18, 25, 20), (18, 25, 20)))
# Get local fsc or randomized map
dc = mam_dc.deep_copy()
dc.map_manager().set_wrapping(False)
map_coeffs = dc.map_manager().map_as_fourier_coefficients(d_min=3)
from cctbx.development.create_models_or_maps import generate_map
new_mm_1 = generate_map(map_coeffs=map_coeffs,
d_min=3,
low_resolution_real_space_noise_fraction=1,
high_resolution_real_space_noise_fraction=50,
map_manager=dc.map_manager(),
random_seed=124321)
new_mm_2 = generate_map(map_coeffs=map_coeffs,
d_min=3,
low_resolution_real_space_noise_fraction=1,
high_resolution_real_space_noise_fraction=50,
map_manager=dc.map_manager(),
random_seed=734119)
dc.add_map_manager_by_id(new_mm_1, 'map_manager_1')
dc.add_map_manager_by_id(new_mm_2, 'map_manager_2')
cc = dc.map_map_cc()
fsc_curve = dc.map_map_fsc()
dc.set_log(sys.stdout)
dc.local_fsc(n_boxes=1)
# Get map-map FSC
dc = mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager', new_map_id='filtered')
dc.resolution_filter(d_min=3.5, d_max=10, map_id='filtered')
dc.create_mask_around_atoms()
fsc_curve = dc.map_map_fsc(map_id_1='map_manager',
map_id_2='filtered',
mask_id='mask',
resolution=3.5,
fsc_cutoff=0.97)
assert approx_equal(fsc_curve.d_min, 3.91175024213, eps=0.01)
assert approx_equal(fsc_curve.fsc.fsc[-1], 0.695137718033)
# Get map-map CC
dc = mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager', new_map_id='filtered')
dc.resolution_filter(d_min=3.5, d_max=6, map_id='filtered')
cc = dc.map_map_cc('map_manager', 'filtered')
assert approx_equal(cc, 0.706499206126)
# Get map-map CC with mask
dc = mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager', new_map_id='filtered')
dc.create_mask_around_density(mask_id='filtered')
cc = dc.map_map_cc('map_manager', 'filtered', mask_id='mask')
assert approx_equal(cc, 0.411247493741)
# box around model
mam = mam_dc.deep_copy()
mam.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1 = mam.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((18, 25, 20), (24, 20, 20)))
# extract_around_model (get new mam)
new_mam_dc = mam_dc.extract_all_maps_around_model(
selection_string="resseq 221:221")
new_mm_1a = new_mam_dc.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1a.map_data().all()),
((18, 25, 20), (24, 20, 20)))
assert approx_equal(new_mm_1.map_data(), new_mm_1a.map_data())
# box around_density
mam2 = mam_dc.deep_copy()
mam2.box_all_maps_around_density_and_shift_origin(box_cushion=0)
new_mm_2 = mam2.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_2.map_data().all()),
((18, 25, 20), (16, 23, 18)))
# extract_around_density (get new mam)
mam2 = mam_dc.deep_copy()
mam2_b = mam2.extract_all_maps_around_density(box_cushion=0)
new_mm_2 = mam2_b.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_2.map_data().all()),
((18, 25, 20), (16, 23, 18)))
# Repeat as map_model_manager:
mmm = mam_dc.as_map_model_manager().deep_copy()
mmm.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1a = mmm.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1a.map_data().all()),
((24, 20, 20), (24, 20, 20)))
assert approx_equal(new_mm_1.map_data(), new_mm_1a.map_data())
# box around density
mam.box_all_maps_around_density_and_shift_origin(box_cushion=0)
new_mm_1 = mam.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((24, 20, 20), (22, 18, 18)))
# extract around density (get new mam)
mam1 = mam_dc.deep_copy()
mam1.extract_all_maps_around_density(box_cushion=0)
new_mm_1 = mam1.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((24, 20, 20), (18, 25, 20)))
# create mask around density, then box around mask (i.e., box around density)
mam.create_mask_around_density(soft_mask=False)
mam.box_all_maps_around_mask_and_shift_origin(box_cushion=3)
new_mm_1 = mam.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((24, 20, 20), (22, 18, 18)))
# box with bounds
mam.box_all_maps_with_bounds_and_shift_origin(lower_bounds=(10, 10, 10),
upper_bounds=(15, 15, 15))
new_mm_1 = mam.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((24, 20, 20), (6, 6, 6)))
# extract with bounds
mam = mam_dc.deep_copy()
mam_1 = mam.extract_all_maps_with_bounds(lower_bounds=(10, 10, 10),
upper_bounds=(15, 15, 15))
new_mm_1 = mam_1.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((24, 20, 20), (6, 6, 6)))
# box with unique
mam = mam_dc.deep_copy()
mam.box_all_maps_around_unique_and_shift_origin(molecular_mass=2500,
resolution=3)
new_mm_1 = mam.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((24, 20, 20), (18, 25, 20)))
# extract with unique
mam = mam_dc.deep_copy()
mam_1 = mam.extract_all_maps_around_unique(molecular_mass=2500,
resolution=3)
new_mm_1 = mam_1.map_manager()
assert approx_equal((mmm.map_data().all(), new_mm_1.map_data().all()),
((24, 20, 20), (18, 25, 20)))
# extract a box and then restore model into same reference as current mam
mam = mam_dc.deep_copy()
mam.box_all_maps_with_bounds_and_shift_origin(lower_bounds=(2, 2, 2),
upper_bounds=(17, 17, 17))
print("mam:",
mam.model().get_sites_cart()[0],
mam.map_manager().origin_is_zero())
# extract a box
box_mam = mam.extract_all_maps_with_bounds(lower_bounds=(10, 10, 10),
upper_bounds=(15, 15, 15))
box_model = box_mam.model()
matched_box_model = mam.get_model_from_other(box_mam)
assert approx_equal(matched_box_model.get_sites_cart()[0],
mam.model().get_sites_cart()[0])
# Convert a map to fourier coefficients
mam = mam_dc.deep_copy()
ma = mam.map_as_fourier_coefficients(d_min=3)
assert approx_equal(ma.d_min(), 3.01655042414)
mam.add_map_from_fourier_coefficients(ma, map_id='new_map_manager')
cc = flex.linear_correlation(
mam.get_map_manager_by_id('map_manager').map_data().as_1d(),
mam.get_map_manager_by_id(
'new_map_manager').map_data().as_1d()).coefficient()
assert (cc >= 0.99)
# Get map-model CC
dc = mam_dc.extract_all_maps_around_model(
selection_string="(name ca or name cb or name c or name o) " +
"and resseq 221:221",
box_cushion=0)
cc = dc.map_model_cc(resolution=3)
assert approx_equal(cc, 0.450025539936)
# Remove model outside map
dc.remove_model_outside_map(boundary=0)
assert (mam_dc.model().get_sites_cart().size(),
dc.model().get_sites_cart().size()) == (86, 4)
# shift a model to match the map
dc = mam_dc.extract_all_maps_around_model(
selection_string="(name ca or name cb or name c or name o) " +
"and resseq 221:221",
box_cushion=0)
actual_model = dc.model().deep_copy()
working_model = dc.model().deep_copy()
working_model.set_shift_cart((0, 0, 0))
working_model.set_sites_cart(working_model.get_sites_cart() -
actual_model.shift_cart())
dc.shift_any_model_to_match(working_model)
assert approx_equal(actual_model.get_sites_cart()[0],
working_model.get_sites_cart()[0])
